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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Filtering micro-operations for a micro-operation cache in a processor

A processor includes a micro-operation cache having a plurality of micro-operation cache entries for storing micro-operations decoded from instruction groups and a micro-operation filter having a plurality of micro-operation filter table entries for storing identifiers of instruction groups for which the micro-operations are predicted dead on fill if stored in the micro-operation cache. The micro-operation filter receives an identifier for an instruction group. The micro-operation filter then prevents a copy of the micro-operations from the first instruction group from being stored in the micro-operation cache when a micro-operation filter table entry includes an identifier that matches the first identifier.

Scrbak, Marko↗

NASA NTRS2024

Operational Integration Assessment (OIA) of Midterm UAM Operations: Class C Airspace Tabletop Exercise and Integration Checkpoint

The National Aeronautics and Space Administration (NASA), in collaboration with the Federal Aviation Administration (FAA), is conducting research into evolving today’s air traffic management system towards a more automated and operationally flexible airspace to accommodate Urban Air Mobility (UAM) operations at scale. UAM operations, enabled by electric Vertical Takeoff and Landing (eVTOL) aircraft, may change the role of aviation in the movement of people and goods and provide practical, cost-effective air transport in metropolitan areas. FAA UAM Concept of Operations v2.0 describes three evolutionary stages of UAM operations: Initial, Midterm, and Mature State operations. Midterm operations are comprised of many complex changes to the national airspace system (NAS). The Operational Integration Assessment (OIA) was created as a capability to address the need to study the progression and identify interdependencies of those changes that may occur during the midterm UAM operations timeframe. The OIA includes a series of tabletop exercises and integration checkpoints planned to explore various use cases from end-to-end, evaluated by NASA’s Air Traffic Management eXploration (ATM-X) project in partnership with the FAA’s William J. Hughes Technical Center (WJHTC) and industry partners. The use cases were exercised in an immersive, integrated live-virtual-constructive (LVC) airspace simulation environment, called the NASA/FAA Laboratory Integrated Test Environment (NFLITE), as part of an effort to learn how UAM operations can scale beyond the as-is NAS and through the transition to higher-tempo and highly automated operations of the future. This document describes the events of the tabletop exercise held from January 24-26, 2023, at the National Airspace Research & Technology Park (NARTP) in Egg Harbor Township, New Jersey, adjacent to the WJHTC and the subsequent integration checkpoint performed on March 28, 2023,at NASA Langley Research Center (LaRC) in Hampton, Virginia.

UAM↗

DOE OSTI2023

Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework

In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Forecasting Solar-Thermal Systems Performance under Transient Operation Using a Data-Driven Machine Learning Approach Based on the Deep Operator Network Architecture

Modeling and prediction of the dynamic behavior of thermal systems operating under intermittent energy input and variable load requirements represent one of the greatest challenges in the development of efficient and reliable renewable-based power generation technologies. In this work, a data-driven machine learning modeling framework was developed based on a modified version of the Deep Operator Network architecture where the time coordinate in the trunk net is replaced with historical data of the predicting quantity. The modeling framework can be used to accurately predict the performance of renewable-based energy conversion technologies including wind- and solar-based power plants. This novel framework was applied on a solar-thermal system that consists of a solar collection loop using a flat plate collector, a power generation loop comprising an Organic Rankine Cycle, and a thermal energy storage tank connecting both loops. Variable solar irradiance, air temperature, and power load profiles were used by the Deep Operator Network to predict the State-of-Charge and the efficiency of the thermal system for several days. The results were compared with the State-of-Charge and efficiency functions calculated using a physics-based model. For a simple operation scenario, characterized by a clear sky solar irradiance profile and constant load, the standard deviation in the State-of-Charge prediction by Deep Operator Network is below 0.9% during a seven-day prediction time horizon. For the most realistic operation scenario that considers real solar irradiance and a rough load profile, the maximum standard deviation in the predictions for the State-of-Charge and efficiency are below 6.8% and 2.5%, respectively. A comparison between Deep Operator Network and Long Short Term Memory network was also performed. In general, both networks predict very well the State-of-Charge for different data density conditions; however, a higher accuracy, with a standard deviation below 2.0%, is obtained by the Deep Operator Network during three and half days using sparser training data of 20-minute points. The same accuracy for the State-of-Charge prediction with the Long Short Term Memory network is achieved only for 14 h. Average standard deviations for the State-of-Charge prediction of 1.1% with the Deep Operator Network and 1.5% with the Long Short Term Memory network are obtained for a four-day prediction time using a denser training data of 5-minute points.

DeepONet↗

DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗

NASA NTRS1997

The Effects of the NASA Space Operations Approaches to Reduce Operations Costs on International Space Communications and Operations Cooperation for Communications and Robotic Missions

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reduce costs contractor function service provider ↗

NASA NTRS1975

IUS/TUG orbital operations and mission support study. Volume 3: Space tug operations

A study was conducted to develop space tug operational concepts and baseline operations plan, and to provide cost estimates for space tug operations. Background data and study results are presented along with a transition phase analysis (the transition from interim upper state to tug operations). A summary is given of the tug operational and interface requirements with emphasis on the on-orbit checkout requirements, external interface operational requirements, safety requirements, and system operational interface requirements. Other topics discussed include reference missions baselined for the tug and details for the mission functional flows and timelines derived for the tug mission, tug subsystems, tug on-orbit operations prior to the tug first burn, spacecraft deployment and retrieval by the tug, operations centers, mission planning, potential problem areas, and cost data.

Source record↗

NASA NTRS1993

NASDA satellite mission operation system and operations

NASDA has recently developed a new tracking and control system as a basis for future satellite mission operation. It is named type-I Space Operations and Data Systems (type-I SODS). The software of this system is separated into three parts: operation and control system, network system, and support and information system. The operation control system treats telemetry and command operations. The network system controls the communication line and ground station equipments to connect the satellite and the operation control system. The support and information system provides to other systems necessary information. JERS-1 which was launched in February of this year is the first satellite operated by type-l SODS. We explain the architecture and operation methods of this system using JERS-1 mission operations.

Yamaya, Kousaku↗

NASA NTRS2005

The Small Aircraft Transportation System (SATS), Higher Volume Operations (HVO) Off-Nominal Operations

The ability to conduct concurrent, multiple aircraft operations in poor weather, at virtually any airport, offers an important opportunity for a significant increase in the rate of flight operations, a major improvement in passenger convenience, and the potential to foster growth of charter operations at small airports. The Small Aircraft Transportation System, (SATS) Higher Volume Operations (HVO) concept is designed to increase traffic flow at any of the 3400 nonradar, non-towered airports in the United States where operations are currently restricted to one-in/one-out procedural separation during Instrument Meteorological Conditions (IMC). The concept's key feature is pilots maintain their own separation from other aircraft using procedures, aircraft flight data sent via air-to-air datalink, cockpit displays, and on-board software. This is done within the Self-Controlled Area (SCA), an area of flight operations established during poor visibility or low ceilings around an airport without Air Traffic Control (ATC) services. The research described in this paper expands the HVO concept to include most off-nominal situations that could be expected to occur in a future SATS environment. The situations were categorized into routine off-nominal operations, procedural deviations, equipment malfunctions, and aircraft emergencies. The combination of normal and off-nominal HVO procedures provides evidence for an operational concept that is safe, requires little ground infrastructure, and enables concurrent flight operations in poor weather.

Baxley, B.↗

NASA NTRS2012

Managing Science Operations during Planetary Surface Operations at Long Light Delay-Time Targets: The 2011 Desert RATS Test

Desert Research and Technology Studies (Desert RATS) is a multi-year series of hardware and operations tests carried out annually in the high desert of Arizona in the San Francisco Volcanic Field. Conducted since 1997, these activities are designed to exercise planetary surface hardware and operations in conditions where multi-day tests are achievable. Desert RATS 2011 Science Operations Test simulated the management of crewed science operations at targets that were beyond the light delay time experienced during Low-Earth Orbit (LEO) and lunar surface missions, such as a mission to a Near-Earth Object (NEO) or the martian surface. Operations at targets at these distances are likely to be the norm as humans move out of the Earth-Moon system. Operating at these distances places significant challenges on mission operations, as the imposed light-delay time makes normal, two-way conversations extremely inefficient. Consequently, the operations approach for space missions that has been exercised during the first half-century of human space operations is no longer viable, and new approaches must be devised.

Eppler, D. B.↗

NASA NTRSDate not supplied

Mission Operations, Cubed: NASA Marshall Operations Support for SmallSats

SmallSats have come a long way since the Huntsville Operations Support Center (HOSC) at NASA’s Marshall Space Flight Center supported its first “minisatellite” mission in 2010. And just as SmallSats themselves have evolved in those 12 years, so too has the HOSC’s mission support for SmallSats. Marshall Space Flight Center has a long history with payload and mission operations, including support for the Apollo missions to the moon, the Space Shuttle program, and 21 years of continuous around-the-clock science operations support for research aboard the International Space Station. Today, the HOSC is a multi-tenant facility, supporting not only ISS, but also NASA’s Commercial Crew program, the Space Launch System, the Hubble and Chandra observatories and others – including multiple SmallSat missions. Two SmallSat solar sail missions will be among those taking advantage of the HOSC’s resources for planning, training for and executing mission operations – the Near Earth Asteroid (NEA) Scout and Solar Cruiser missions. One of 10 6U CubeSats manifest on the Artemis I launch of NASA’s Space Launch System rocket this year, NEA Scout’s three-year mission will be supported through a more traditional operations concept, with a dedicated Flight Controller staff operating within the HOSC. Scheduled to launch as part of the Interstellar Mapping and Acceleration Probe (IMAP) in February 2025, Solar Cruiser’s 11-month mission will take a next-generation approach to operations by utilizing a multi-mission flight controller concept, as well as Marshall’s Telescience Resource Kit (TreK). TreK provides a suite of software applications and libraries that allow the Mission Operations Center to serve as an in-house ground system which incorporates remote and automation capability options for engineers and scientists. This presentation will compare the approaches the HOSC will use to support these two missions as a way of demonstrating the array of options NASA MSFC offers for operations support for CubeSat and SmallSat missions.

Darren S Wallace↗

NASA NTRSDate not supplied

Insights and Observations from Operating a Geostationary Laser Communication Relay Mission: Operational Lessons from NASA’s Laser Communications Relay Demonstration (LCRD) and Associated Optical Ground Stations (OGSs)

The NASA Laser Communications Relay Demonstration (LCRD) has operated on orbit for the last two years. The LCRD Mission consists of a geostationary payload and two optical ground stations. This technology demonstration mission is NASA’s first two-way, end-to-end optical communications relay. LCRD has performed an extensive experiment campaign to analyze laser communication performance for extended operations. This paper discusses various lessons learned while operating the optical relay mission from early commissioning through two years of operation. Before launch, engineers and operators performed analyses to generate operational procedures based on expected LCRD performance. However, only operation on orbit can supply actual performance data. Additional topics covered in this paper include pre-launch and commissioning testing, ephemeris generation and cadence, environmental configurations, and accommodation concerns. The addition of Integrated Laser Communications Relay Demonstration Low Earth Orbit User Modem and Amplifier Terminal (ILLUMA-T) to the optical network supplemented the LCRD team’s operational experiences and enabled them to garner new lessons learned. The authors of this paper include day-to-day flight leads who consulted with on-console operators, engineers, and subject matter experts analyzing data and experiment results to gather the lessons learned described in this paper. LCRD is a joint project involving NASA Goddard Space Flight Center (GSFC), the California Institute of Technology Jet Propulsion Laboratory (JPL), and Massachusetts Institute of Technology Lincoln Laboratory (MIT LL).

Lessons Learned↗

NASA NTRS2014

A Concept of Operations for Far-Term Surface Trajectory-Based Operations (STBO)

The goal of this far-term STBO (Surface Trajectory-Based Operations) ConOps (Concept of Operations) is to increase the efficiency and predictability of airport surface operations, and reduce the environmental impact, by incorporating a time-based component to surface operations. In the far-term NextGen timeframe, airport surface operations will transition from current-day first-come, first-served operations, to strategically scheduled operations in which pilots are recruited as active participants in meeting the precise time-based goals of NextGen surface operations. The far-term STBO concept includes two-phases. Phase 1 introduces time-based traffic flow constraint points, which divide the taxi route into segments with an assigned Required Time of Arrival (RTA). This Phase 1 approach provides temporal certainty only near the traffic flow constraint points, but not in between. Minimal augmentations to the flight deck are required to support required time of arrival (RTA) management. Phase 2 further increases precision and efficiency by introducing full four-dimensional (4D) trajectories, with an x-y location for all times t. This phase assumes adoption of advanced flight deck equipage enabling higher temporal precision sufficient to support aircraft conformance to 4D trajectories. This allows more precision and less temporal uncertainty at all times along the route.

Surface Trajectory-Based Operations (STBO)↗

NASA NTRSDate not supplied

Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment

Many envisioned technological and conceptual innovations focus on allocating more functions to automation, relegating the human as an afterthought if not a nuisance. Yet, until complete autonomy is realized, the human will remain “in the loop”. The National Aeronautics and Space Administration is supporting research for the development and maturation of automated technologies and architectures for the future of advanced air mobility. Standing up a remote operations center used to control, manage, and monitor multiple highly automated vehicles is an important step towards realizing the advanced air mobility vision. At the National Aeronautics and Space Administration’s Langley Research Center, a remote operation center exists and has been tested using humans piloting simulated vehicles. In this paper, we explore the human element within live flight operations that rely on increasingly automated technologies. Four ground control station operators performed multiple live flight operations. We employed a naturalistic approach and relied on qualitative data such as interviews and discussions with subject matter experts to help facilitate discovery. The present work evaluates the functions that the human and the automation had during the live operations, lists psychological constructs that may have promoted or reduced task performance, and provides recommendations on design of the remote operations environment and training of future ground control station operators.

Advanced Air Mobility↗

NASA NTRSDate not supplied

Where is the Human in the Loop? Human Factors Analysis of Extended Visual Line of Sight Unmanned Aerial System Operations within a Remote Operations Environment

Advanced Air Mobility↗

DOE OSTI2022

E-transit-bench: simulation platform for analyzing electric public transit bus fleet operations

When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid's power flow and therefore cannot account for the power grid's needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day's traffic from Chattanooga city's transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.

Sen, Rishav↗

NASA NTRS1975

IUS/TUG orbital operations and mission support study. Volume 2: Interim upper stage operations

Background data and study results are presented for the interim upper stage (IUS) operations phase of the IUS/tug orbital operations study. The study was conducted to develop IUS operational concepts and an IUS baseline operations plan, and to provide cost estimates for IUS operations. The approach used was to compile and evaluate baseline concepts, definitions, and system, and to use that data as a basis for the IUS operations phase definition, analysis, and costing analysis. Both expendable and reusable IUS configurations were analyzed and two autonomy levels were specified for each configuration. Topics discussed include on-orbit operations and interfaces with the orbiter, the tracking and data relay satellites and ground station support capability analysis, and flight control center sizing to support the IUS operations.

Source record↗

NASA NTRS1990

Operationally Efficient Propulsion System Study (OEPSS) data book. Volume 3: Operations technology

The study was initiated to identify operational problems and cost drivers for current propulsion systems and to identify technology and design approaches to increase the operational efficiency and reduce operations costs for future propulsion systems. To provide readily usable data for the Advanced Launch System (ALS) program, the results of the OEPSS study were organized into a series of OEPSS Data Books. This volume describes operations technologies that will enhance operational efficiency of propulsion systems. A total of 15 operations technologies were identified that will eliminate or mitigate operations problems described in Volume 2. A recommended development plan is presented for eight promising technologies that will simplify the propulsion system and reduce operational requirements.

Vilja, John O.↗